Print

Print


*****  To join INSNA, visit http://www.insna.org  *****


===================

Barry Wellman, FRSC               Director, NetLab Network
Founder, International Network for Social Network Analysis

Bit by bit, putting it together--Sondheim
It's Always Something--Roseanne Roseannadanna

Getting It Done; Getting It Out: A Practical Guide to Writing, Publishing, Presenting and Promoting in the Social Sciences--coming in 2021 (Guilford Press)

NETWORKED: The New Social Operating System  Lee Rainie & Barry Wellman https://urldefense.proofpoint.com/v2/url?u=http-3A__amzn.to_zXZg39&d=DwIDaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=PRgmCM3nA4efpmcQpXi64rDTazj7J9Xn9Z7ZXG5IJAM&s=HfXOFMeoTl4jJVRyGvTtlcNUcuydgnYkB4EfDKDNjBE&e= 
https://urldefense.proofpoint.com/v2/url?u=http-3A__www.chass.utoronto.ca_-7Ewellman&d=DwIDaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=PRgmCM3nA4efpmcQpXi64rDTazj7J9Xn9Z7ZXG5IJAM&s=3ldsh2b6kLHn8_kQtbzqB6ld97vubYS3jQBndoUl29k&e=             https://urldefense.proofpoint.com/v2/url?u=https-3A__en.wikipedia.org_wiki_Barry-5FWellman&d=DwIDaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=PRgmCM3nA4efpmcQpXi64rDTazj7J9Xn9Z7ZXG5IJAM&s=QJVS8vGw6sRqi12ZKw1wiO2RcIhqnbO8jviWC0ToIUk&e= 



-------- Forwarded Message --------
Subject: 	Latest Complexity Digest Posts
Date: 	Mon, 15 Jun 2020 11:02:31 +0000
From: 	Complexity Digest <[log in to unmask]>
Reply-To: 	[log in to unmask]
To: 	Barry <[log in to unmask]>



Learn about the latest and greatest related to complex systems research. 
More at 
https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3Dd9748e1f1c-26e-3D55e25a0e3e&d=DwIDaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=PRgmCM3nA4efpmcQpXi64rDTazj7J9Xn9Z7ZXG5IJAM&s=ehMkrEP6S8P_KKE3BgdLc0VS3SFtqrqfQXA-gAOHJqA&e= 



Building the New Economy ·

https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D31a72d25ba-26e-3D55e25a0e3e&d=DwIDaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=PRgmCM3nA4efpmcQpXi64rDTazj7J9Xn9Z7ZXG5IJAM&s=Eeb9poj8T4oLOQ6vmVvPMSItp-n6nnWGK6kHp6cnwfo&e= 

Edited by Alex Pentland, Alexander Lipton, and Thomas Hardjono

With each major crisis, be it war, pandemic, or major new technology, 
there has been a need to reinvent the relationships between individuals, 
businesses, and governments. Today's pandemic, joined with the tsunami 
of data, crypto and AI technologies, is such a crisis. Consequently the 
critical question for today is: what sort institutions should we be 
creating both to help us past this crisis and to make us less vulnerable 
to the next crisis? This book lays out a vision of what we should build, 
covering not only how to reforge our societies' social contract but also 
how institutions, systems, infrastructure, and law should change in 
support of this new order. We invite your comments and suggestions on 
both the ideas and the presentation, preferably by June 1, 2020 when we 
will move to make the book more widely available.

Source: wip.mitpress.mit.edu 
(https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D75c716f76a-26e-3D55e25a0e3e&d=DwIDaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=PRgmCM3nA4efpmcQpXi64rDTazj7J9Xn9Z7ZXG5IJAM&s=bxahvSq0y3-z3n3cSLUgqbtJHObGMABoa_mpQITmCn0&e= )


Data-Driven Learning of Boolean Networks and Functions by Optimal 
Causation Entropy Principle (BoCSE)

Jie Sun, Abd AlRahman AlMomani, Erik Bollt


Boolean functions and networks are commonly used in the modeling and 
analysis of complex biological systems, and this paradigm is highly 
relevant in other important areas in data science and decision making, 
such as in the medical field and in the finance industry. Automated 
learning of a Boolean network and Boolean functions, from data, is a 
challenging task due in part to the large number of unknowns (including 
both the structure of the network and the functions) to be estimated, 
for which a brute force approach would be exponentially complex. In this 
paper we develop a new information theoretic methodology that we show to 
be significantly more efficient than previous approaches. Building on 
the recently developed optimal causation entropy principle (oCSE), that 
we proved can correctly infer networks distinguishing between direct 
versus indirect connections, we develop here an efficient algorithm that 
furthermore infers a Boolean network (including both its structure and 
function) based
on data observed from the evolving states at nodes. We call this new 
inference method, Boolean optimal causation entropy (BoCSE), which we 
will show that our method is both computationally efficient and also 
resilient to noise. Furthermore, it allows for selection of a set of 
features that best explains the process, a statement that can be 
described as a networked Boolean function reduced order model. We 
highlight our method to the feature selection in several real-world 
examples: (1) diagnosis of urinary diseases, (2) Cardiac SPECT 
diagnosis, (3) informative positions in the game Tic-Tac-Toe, and (4) 
risk causality analysis of loans in default status. Our proposed method 
is effective and efficient in all examples.

Source: arxiv.org 
(https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D9cea45760f-26e-3D55e25a0e3e&d=DwIDaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=PRgmCM3nA4efpmcQpXi64rDTazj7J9Xn9Z7ZXG5IJAM&s=cwToSPtJmndhqBvoI0zseKTmHbGKx8833PW7wgjgQe4&e= )



Uncovering the social interaction network in swarm intelligence algorithms

https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3Dff95389bed-26e-3D55e25a0e3e&d=DwIDaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=PRgmCM3nA4efpmcQpXi64rDTazj7J9Xn9Z7ZXG5IJAM&s=J1zWdubiVTlnZzSATaMD8qwjg9Dw7AY10X2XVrny5RQ&e= 

Marcos Oliveira, Diego Pinheiro, Mariana Macedo, Carmelo Bastos-Filho & 
Ronaldo Menezes
Applied Network Science volume 5, Article number: 24 (2020)


Swarm intelligence is the collective behavior emerging in systems with 
locally interacting components. Because of their self-organization 
capabilities, swarm-based systems show essential properties for handling 
real-world problems, such as robustness, scalability, and flexibility. 
Yet, we fail to understand why swarm-based algorithms work well, and 
neither can we compare the various approaches in the literature. The 
absence of a common framework capable of characterizing these several 
swarm-based algorithms, transcending their particularities, has led to a 
stream of publications inspired by different aspects of nature without a 
systematic comparison over existing approaches. Here we address this gap 
by introducing a network-based framework—the swarm interaction 
network—to examine computational swarm-based systems via the optics of 
the social dynamics. We investigate the structure of social interaction 
in four swarm-based algorithms, showing that our approach enables 
researchers to study
distinct algorithms from a common viewpoint. We also provide an in-depth 
case study of the Particle Swarm Optimization, revealing that different 
communication schemes tune the social interaction in the swarm, 
controlling the swarm search mode. With the swarm interaction network, 
researchers can study swarm algorithms as systems, removing the 
algorithm particularities from the analyses while focusing on the 
structure of the swarm social interaction.

Source: appliednetsci.springeropen.com 
(https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3De0e4361aaf-26e-3D55e25a0e3e&d=DwIDaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=PRgmCM3nA4efpmcQpXi64rDTazj7J9Xn9Z7ZXG5IJAM&s=EBaegCBbM-W4zfI8aVNenkCWKZPmM5AAJr425HCK1hw&e= )


Joint estimation of non-parametric transitivity and preferential 
attachment functions in scientific co-authorship networks

Masaaki Inoue, Thong Pham, Hidetoshi Shimodaira

Journal of Informetrics
Volume 14, Issue 3, August 2020, 101042


• Transitivity and preferential attachment exist jointly in two 
co-authorship networks.

• Neither alone could describe the networks well.

• Their functional forms deviate substantially from the conventional 
power-law form.

• Transitivity greatly dominated preferential attachment in both networks.

Source: https://urldefense.proofpoint.com/v2/url?u=http-3A__www.sciencedirect.com&d=DwIDaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=PRgmCM3nA4efpmcQpXi64rDTazj7J9Xn9Z7ZXG5IJAM&s=bReuNdQnwtjKYrz82b17KzoCbE3_HLmPBiKjsrNUa9Y&e=  
(https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D06711bbf09-26e-3D55e25a0e3e&d=DwIDaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=PRgmCM3nA4efpmcQpXi64rDTazj7J9Xn9Z7ZXG5IJAM&s=gJ8WtvJTibbsNaJkGVgcAkjIWRJwz0TmL4dfy1zmq30&e= )



==============================================
Sponsored by the Complex Systems Society.
Founding Editor: Gottfried Mayer.
Editor-in-Chief: Carlos Gershenson.

You can contribute to Complexity Digest selecting one of our topics 
(https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D3dcd7ac746-26e-3D55e25a0e3e&d=DwIDaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=PRgmCM3nA4efpmcQpXi64rDTazj7J9Xn9Z7ZXG5IJAM&s=fMinnQkgRYzpZVE2yu-rpXzRZY0XpsV1rMlxeV8RILI&e=  
) and using the "Suggest" button.
==============================================
==============================================



_____________________________________________________________________
SOCNET is a service of INSNA, the professional association for social
network researchers (http://www.insna.org). To unsubscribe, send
an email message to [log in to unmask] containing the line
UNSUBSCRIBE SOCNET in the body of the message.